---
title: "Hands-on Exercise 3"
author: "Chang Fang Yu"
date-modified: "last-modified"
execute:
echo: True
eval: True
warning: false
freeze: true
---
# Programming Interactive Data Visualisation with R
## Getting Started
### Installing and loading the required libraies
```{R}
#| message: false
#| warning: false
pacman::p_load(ggiraph, plotly,
patchwork, DT, tidyverse)
```
### Importing Data
```{R}
#| message: false
#| warning: false
exam_data <- read_csv("data/Exam_data.csv")
```
## Interactive Data Visualisation - ggiraph methods
### Tooltip effect with tooltip aesthetic
```{R}
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)
```
## Interactivity
### Displaying multiple information on tooltip
```{R, fold: true}
exam_data$tooltip <- c(paste0(
"Name = ", exam_data$ID,
"\n Class = ", exam_data$CLASS))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = exam_data$tooltip),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 8,
height_svg = 8*0.618
)
```
### Displaying statistics on tooltip
```{R}
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, linewidth = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)
```
### Hover effect with data_id aesthetic
```{R}
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)
```
### Styling hover effect
```{R}
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
```
### Combining tooltip and hover effect
```{R}
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
```
### Click effect with onclick
```{R}
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618)
```
### Coordinated Multiple Views with ggiraph
```{R}
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 + p2),
width_svg = 6,
height_svg = 3,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
```
## Interactive Data Visualisation - plotly methods!
### Creating an interactive scatter plot: plot_ly() method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)
```
:::
### Working with visual variable: plot_ly() method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)
```
:::
### Creating an interactive scatter plot: ggplotly() method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)
```
:::
## Interactive Data Visualisation - crosstalk methods!
### Interactive Data Table: DT package
```{R}
DT::datatable(exam_data, class= "compact")
```
### Linked brushing: crosstalk method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5)
```
:::
# Programming Animated Statistical Graphics with R
## Getting started
```{R}
pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)
```
### Importing the data
```{R}
#| message: false
#| warning: false
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate(across(col, as.factor)) %>%
mutate(Year = as.integer(Year))
```
## Animated Data Visualisation: gganimate methods
### Building a static population bubble plot
```{R}
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young')
```
### Building the animated bubble plot
```{R}
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear')
```
## Animated Data Visualisation: plotly
### Building an animated bubble plot: ggplotly() method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')
ggplotly(gg)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')
ggplotly(gg)
```
:::
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')
ggplotly(gg)
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')
ggplotly(gg)
```
:::
### Building an animated bubble plot: plot_ly() method
::: panel-tabset
### The plot
```{R}
#| echo: false
#| message: false
#| warning: false
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
bp
```
### The code
```{R}
#| echo: true
#| message: false
#| warning: false
#| fig.show: "hide"
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
bp
```
:::
